Several attempts have been made to design a system to automate the process of analyzing a signal input concerning an activity. There is also a desire to utilize input (e.g., from visual or bio-sensor sources) for a detection systems to facility the monitoring of repetitious movement in live or recorded content, such as in pre-captured or real-time video. This content may capture a scene in which the same action is repeated multiple times in consecutive cycles of a relatively uniform length and there is a desire to count the number of repetitions. For example, there may be a desire to count the number of times a bird flapping its wings, a hand strums a guitar, or the number of repetitions performed of the same physical exercise. There is no limit to the number of repetitious actions for which automated counting of a capture visual input might be desired.
Prior techniques for utilizing visual count of repetitious activity with a system for automating the analysis of the content include, U.S. Pat. No. 8,165,349 to Bobbitt et al., which discloses “[t] echniques for analyzing one or more sequential events performed by a human actor to evaluate efficiency of the human actor.” This patent does not disclose any technique for actually counting of the observable repetitions performed by the actor, let alone an accurate automated counting system. U.S. Publication No. 20140369561 to Gupta et al. discloses “a method and a system for enhancing accuracy of human counting in at least one frame of a captured image in a real-time in a predefined area.” This publication discloses a method for detecting and counting of the number of humans in image content, not the counting of repetitions of an action performed by a human, or any other actor for that matter.
U.S. Publication No. 20140270375 to Canavan et. al. discloses “[a] motion tracking system [that] monitors the motions performed by a user based on motion data received from one or more sensors.” The only counting method disclosed in this publication involves a “template matching algorithm to identify repetitive features . . . [which] compares the repetitive features to a set of movement templates 27 stored in a movement template database.” This counting mechanism taught by this publication, therefore, requires predetermination and analysis of specific movements that will be capable of being monitored by the system. The movement example given is a repeated curl exercise. There are several drawback to the system disclosed by Canavan et al. as it relates to an attempt to automatically count the number of repetitions performed by an actor, not the least of which is the requirement that the action to be counted must have been pre-analyzed and algorithmically tabulated before any counting may be accomplished.
Moreover, most video analytics systems require or assume that the visual content to be analyzed is segmented in time. These analytics tools do not enable the automatic detection of the start and end points of the sequence of repetitive actions to be machine counted, for example, in live video streaming. Nor do these systems enable for the counting to start during the period when the repetitive action is still initially being detected. None of these systems are directed for use with real world videos, such as non-commercial video content collected by YouTube or other sources, where only a handful of repetitions might be identified, the cycle length changes significantly throughout the video, and each repetition is often visually different from the other repetitions.
Prior systems are primarily based on frequency domain analysis or on autocorrelation which are unable to work on live visual inputs without post-processing.
What is needed is a better scheme that enables machine counting of repetitious behavior from a wide variety of visual sources without advanced processing or preparation of the source, nor the requirement for post-capture processing of visual content to enable automated counting.